AI Data Pipeline 101: Ingest & Archive Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data, highlighting the importance of data ingest and storage in AI pipelines. Backblaze's in-house AI experts have divided the AI data pipeline into five essential phases, emphasizing that streaming ingestion requires constant low-latency access to storage and high-throughput networking to avoid upload lags and errors. Extensive news coverage and analyst reports on AI missing productivity and ROI targets mean that AI failure is something of a hot topic. There’s no arguing that some AI initiatives are misguided, including replacing entire specialist teams with AI. For others, the issue actually lies with data knowledge and readiness– Gartner predicts https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data, and S&P Global recently highlighted https://www.spglobal.com/market-intelligence/en/news-insights/videos/unlocking-ai-driven-insights-for-risk-management the importance of ingesting previously overlooked or unknown data to discover interdependencies in risk management. The pressure to move at the perceived speed of AI makes it easy to skip or rush important prep work. Now that major AI pilots have been up and running, supplementing or sometimes entirely replacing select business functions with mixed results https://www.spglobal.com/market-intelligence/en/news-insights/research/2025/10/generative-ai-shows-rapid-growth-but-yields-mixed-results , this exposes an already known problem among AI experts: implementing AI too quickly and ignoring the importance of keeping human experts in the loop increases your threshold for error. This is especially prevalent for both internal AI tools that are meant to augment key roles, and customer-facing AI applications that are supposed to increase accessibility to an outcome, such as generating a lifelike video based on natural language prompts. What’s causing this? It’s not just the LLMs–it’s the data. Now that human experts are more aware of what AI can get wrong, we’re going back to basics to help you get it right. This starts at the very beginning: curating, ingesting, and storing data using infrastructure that’s actually designed for moving data quickly, and without financial penalty. The Stages of the AI Model Training Data Pipeline Our in-house AI experts have split the AI data pipeline into five essential key phases https://www.backblaze.com/blog/architecting-your-ai-data-pipeline-using-b2-overdrive/ for model training, and highlighting how storage factors into each stage including what’s being stored. Data collection ≠ data ingest Data ingest is the process of any type of data being added to a designated collection destination, whether that’s a specific file folder, database, or object storage bucket. For a lot of applications, data ingest is frequent or nearly constant–busy e-commerce sites with a constant flow of customer transactions and feedback, live video feeds, and combining real-time data sources like pairing security footage with physical building security sensors. This is called streaming ingestion–and streaming ingestion being the foundation of data collection for various types of AI is one of the key reasons why storage is an AI infrastructure problem that often flies under the radar until there’s a serious problem. - Streaming ingestion requires constant low-latency access to the data storage repository to prevent data upload lags and errors. - Streaming ingestion for video and other large files requires high rate limits and high-throughput networking capabilities to optimize upload times, especially when an application requires a file to be ready for processing in seconds/minutes instead of hours/days. - Running out of storage capacity is not an option for model performance, and for compliance and auditing purposes–streaming ingestion requires constant access to storage that is as close to infinite as possible. Automation from the start In addition to the files themselves, setting up a highly effective AI data pipeline involves building automation from the very beginning to immediately allocate files to the right bucket using taxonomy and collect and store file metadata to begin data aggregation critical for future labeling and processing. Why taxonomy is critical for AI The file itself is the data source. Navigating your dataset starts with implementing a taxonomy that makes your dataset highly searchable as your data grows from a few thousand for your first round of training to millions for an AI application operating in production. Taxonomy is essentially your file storage structure, or how files are automatically “nested” and relationships between files are built immediately upon ingest. When you’re just getting started, developing your taxonomy helps you stay organized and ready to go searching for a specific file when your coworker doesn’t believe what the data is saying. When you’re working in established teams, implementing a new taxonomy or showing that you understand the importance of following an established taxonomy builds a contract of understanding between you and your data engineering or ML teams. AKA, changing taxonomy mid-project is a big undertaking with ripple effects, and should not be taken lightly. A simple example taxonomy for ingesting raw files can look like this: /